When regular Q-learning takes too much time to converge in a high-dimensional state space (e.g., autonomous vehicle parking), what modification could help it learn faster?
- Deep Q-Networks (DQNs)
- Policy Gradient Methods
- Fitted Q-Iteration (FQI)
- Temporal Difference (TD) Learning
Using Deep Q-Networks (DQNs) is a modification of Q-learning, which employs neural networks to handle high-dimensional state spaces efficiently. DQNs can approximate the Q-values, expediting learning in complex environments.
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